Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation.
Powders
/ chemistry
Machine Learning
Wettability
Particle Size
Viscosity
Water
/ chemistry
Molecular Dynamics Simulation
Chemistry, Pharmaceutical
/ methods
Drug Compounding
/ methods
Drugs, Chinese Herbal
/ chemistry
Medicine, Chinese Traditional
/ methods
Excipients
/ chemistry
Permeability
Technology, Pharmaceutical
/ methods
Ethanol
/ chemistry
granule size distribution
high shear wet granulation
machine learning
molecular dynamics simulation
wetting mechanism
Journal
AAPS PharmSciTech
ISSN: 1530-9932
Titre abrégé: AAPS PharmSciTech
Pays: United States
ID NLM: 100960111
Informations de publication
Date de publication:
23 Oct 2024
23 Oct 2024
Historique:
received:
07
08
2024
accepted:
08
10
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
23
10
2024
Statut:
epublish
Résumé
The granulation of traditional Chinese medicine (TCM) has attracted widespread attention, there is limited research on the high shear wet granulation (HSWG) and wetting mechanisms of sticky TCM powders, which profoundly impact the granule size distribution (GSD). Here we investigate the wetting mechanism of binders and the influence of various parameters on the GSD of HSWG and establish a GSD prediction model. Permeability and contact angle experiments combined with molecular dynamics (MD) simulations were used to explore the wetting mechanism of hydroalcoholic solutions with TCM powder. Machine learning (ML) was employed to build a GSD prediction model, feature importance explained the influence of features on the predictive performance of the model, and correlation analysis was used to assess the influence of various parameters on GSD. The results show that water increases powder viscosity, forming high-viscosity aggregates, while ethanol primarily acted as a wetting agent. The contact angle of water on the powder bed was the largest and decreased with an increase in ethanol concentration. Extreme Gradient Boosting (XGBoost) outperformed other models in overall prediction accuracy in GSD prediction, the binder had the greatest impact on the predictions and GSD, adjusting the amount and concentration of adhesive can control the adhesion and growth of granules while the impeller speed had the least influence on granulation. The study elucidates the wetting mechanism and provides a GSD prediction model, along with the impact of material properties, formulation, and process parameters obtained, aiding the intelligent manufacturing and formulation development of TMC.
Identifiants
pubmed: 39443400
doi: 10.1208/s12249-024-02973-w
pii: 10.1208/s12249-024-02973-w
doi:
Substances chimiques
Powders
0
Water
059QF0KO0R
Drugs, Chinese Herbal
0
Excipients
0
Ethanol
3K9958V90M
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
253Informations de copyright
© 2024. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.
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